用神经网络同时估计等离子体信号和噪声，实现快速稳健的实时物理推断。
Heteroscedastic Neural Surrogate Modeling for Robust and Rapid Bayesian Inference in Fusion Plasma Diagnostics
- 双头神经网络同步预测信号与通道噪声方差
- 相比传统方法提速1500倍，误差降低20%以上
- 适合高噪声环境下需要实时分析的物理系统
通过马尔可夫链蒙特卡洛（MCMC）进行贝叶斯推断能有效估计参数，但在复杂物理系统中因计算瓶颈和对统计噪声极度敏感而难以实时应用。本文提出一种基于神经网络的概率代理框架，实现快速且鲁棒的MCMC推断。以融合等离子体汤姆逊散射诊断为高噪声挑战性测试平台，该方法采用双头结构，同时估计期望物理发射谱和各通道内在测量噪声方差。通过优化高斯负对数似然（GNLL）目标函数，学习到的偶然不确定性动态缓冲采样器免受异常噪声干扰。实验表明，该代理框架相较精确物理前向模型实现超过1500倍加速，同时相较于标准同方差神经基线，推断误差（RMSE）降低超过20%，为实时物理分析提供了极具前景的新范式。
原文摘要 · Abstract (English)
Bayesian inference via Markov Chain Monte Carlo (MCMC) provides effective parameter estimation, but its real-time application in complex physical systems is hindered by heavy computational bottlenecks and extreme sensitivity to statistical noise. We address this by proposing a neural-network-based probabilistic surrogate framework for rapid and robust MCMC inference. Using fusion plasma Thomson scattering diagnostics as a challenging, noise-dominated testbed, our approach employs a dual-head architecture to simultaneously estimate the expected physical emission spectrum and the channel-wise intrinsic measurement noise variance. By optimizing a Gaussian Negative Log-Likelihood (GNLL) objective, the learned aleatoric uncertainty dynamically buffers the sampler against pathological shot noise. Evaluations demonstrate that this surrogate framework achieves > 1500x acceleration over exact physical forward models, while simultaneously reducing inference error (RMSE) by >20% compared to standard homoscedastic neural baselines, offering a highly promising paradigm for real-time physical analysis.
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